#!/bin/bash
#当前路径,不需要修改
cur_path=`pwd`
export ASCEND_HOST_LOG_FILE_NUM=1000 
#集合通信参数,不需要修改
RANK_ID_START=0

# 数据集路径,保持为空,不需要修改
data_path="/npu/traindata/imagenet_TF"

#设置默认日志级别,不需要修改
#export ASCEND_GLOBAL_LOG_LEVEL_ETP_ETP=3

#基础参数 需要模型审视修改
#网络名称，同目录名称
Network="EfficientNet_B0_ID0009_for_TensorFlow"
#训练epoch
train_epochs=1
#训练batch_size
batch_size=256
#训练step
train_steps=`expr 1281167 / ${batch_size}`
#学习率
learning_rate=""

#TF2.X独有，不需要修改
export NPU_LOOP_SIZE=${train_steps}

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_mix_precision"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False
autotune=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_full_8p.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode           precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		   data dump flag, default is 0
    --data_dump_step		   data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
    --autotune                 whether to enable autotune, default is False
    --data_path		           source data of training
    -h/--help		           show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --autotune* ]];then
        autotune=`echo ${para#*=}`
        mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
        mv $install_path/fwkacllib/data/tiling/Ascend910/custom $install_path/fwkacllib/data/tiling/Ascend910/custom_bak
        autotune_dump_path=${cur_path}/output/autotune_dump
        mkdir -p ${autotune_dump_path}/GA
        mkdir -p ${autotune_dump_path}/rl
        cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/
        cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --bind_core* ]]; then
        bind_core=`echo ${para#*=}`
        name_bind="_bindcore"
		 elif [[ $para == --server_index* ]];then
        server_index=`echo ${para#*=}`
    elif [[ $para == --conf_path* ]];then
        conf_path=`echo ${para#*=}`
    elif [[ $para == --fix_node_ip* ]];then
	    fix_node_ip=`echo ${para#*=}`
    elif [[ $para == --one_node_ip* ]];then
        one_node_ip=`echo ${para#*=}`
    fi
done

if [[ $conf_path == "" ]];then
    fix_node_ip=$fix_node_ip
    one_node_ip=$one_node_ip
else
    one_node_ip=`find $conf_path -name "server_*_0.info"|awk -F "server_" '{print $2}'|awk -F "_" '{print $1}'`
fi

#新增适配集群环境变量
export CM_CHIEF_IP=${one_node_ip}   #主节点ip，所有服务器一致
export CM_CHIEF_PORT=29688          #通信端口，所有服务器一致
export CM_CHIEF_DEVICE=0            #配置为0，配置主卡，类似于主节点，所有服务器一致
export CM_WORKER_SIZE=32            #卡数，单机为8，多机为8n,所有服务器一致
export CM_WORKER_IP=${fix_node_ip}  #当前服务器ip，不同环境ip不同
#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

#autotune时，先开启autotune执行单P训练，不需要修改
if [[ $autotune == True ]]; then
    train_full_1p.sh --autotune=$autotune --data_path=$data_path
    wait
    autotune=False
fi

#训练开始时间，不需要修改
start_time=$(date +%s)
export RANK_SIZES=32
rank_size=8

#if [[ $conf_path != "" ]];then
#    nohup python3 set_ranktable.py --npu_nums=$((RANK_SIZES/rank_size)) --conf_path=$conf_path
#fi
#
#wait
#export RANK_TABLE_FILE=${cur_path}/rank_table.json
export JOB_ID=10087
export DEVICE_INDEX=0

sed -i 's/RANK_SIZE/RANK_SIZES/g' ../modelarts/start.py ../efficientnet/main_npu.py
sed -i 's/RANK_ID/RANK_IDS/g' ../modelarts/start.py
#进入训练脚本目录，需要模型审视修改
cd $cur_path/../
for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $RANK_ID"
    export RANK_ID=$RANK_ID
    export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))`
    ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` 
    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    fi
    
     # 绑核，不需要的绑核的模型删除，需要模型审视修改
    corenum=`cat /proc/cpuinfo |grep "processor"|wc -l`
    let a=RANK_ID*${corenum}/${RANK_SIZES}
    let b=RANK_ID+1
    let c=b*${corenum}/${RANK_SIZES}-1

    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
    if [ "x${bind_core}" != x ];then
        bind_core="taskset -c $a-$c"
    fi
    nohup ${bind_core} python3.7 efficientnet/main_npu.py \
    --data_dir=${data_path} \
    --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \
    --mode=train_and_eval \
    --train_batch_size=256 \
    --train_steps=500 \
    --iterations_per_loop=100 \
    --steps_per_eval=31250 \
    --eval_batch_size=128 \
    --base_learning_rate=0.2 \
    --model_name=efficientnet-b0  > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))
sed -i 's/RANK_SIZES/RANK_SIZE/g' modelarts/start.py efficientnet/main_npu.py
sed -i 's/RANK_IDS/RANK_ID/g' modelarts/start.py
#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPS=`grep 'logger.py:54' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F 'ips:' '{print $2}'|awk '{print $1}'|awk 'NR>1'|awk '{sum+=$1} END {print sum/NR}'`
#FPS=`grep 'logger.py:54' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F 'ips:' '{print $2}'|awk '{print $1}'|sort -n -r|head -1`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"

#输出训练精度,需要模型审视修改
#train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'`
#打印，不需要修改
#echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#稳定性精度看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZES}'p'_'perf'

##获取性能数据
#吞吐量，不需要修改
ActualFPS=${FPS}
#单迭代训练时长，不需要修改
TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZES}'*1000/'${FPS}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'logger.py:54' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $8}' |awk -F ":" '{print $2}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZES}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log